Datasets:

PolyAI
/

banking77

	---
	annotations_creators:
	- expert-generated
	extended:
	- original
	language_creators:
	- expert-generated
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- text-classification
	task_ids:
	- intent-classification
	- multi-class-classification
	paperswithcode_id: null
	pretty_name: BANKING77
	---

	# Dataset Card for BANKING77

	## Table of Contents
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
	- Repository: [Github](https://github.com/PolyAI-LDN/task-specific-datasets)
	- Paper: [ArXiv](https://arxiv.org/abs/2003.04807)
	- Leaderboard:
	- Point of Contact:

	### Dataset Summary

	Dataset composed of online banking queries annotated with their corresponding intents.

	BANKING77 dataset provides a very fine-grained set of intents in a banking domain.
	It comprises 13,083 customer service queries labeled with 77 intents.
	It focuses on fine-grained single-domain intent detection.

	### Supported Tasks and Leaderboards

	Intent classification, intent detection

	### Languages

	English

	## Dataset Structure

	### Data Instances

	An example of 'train' looks as follows:
	```
	{
	'label': 11, # integer label corresponding to "card_arrival" intent
	'text': 'I am still waiting on my card?'
	}
	```

	### Data Fields

	- `text`: a string feature.
	- `label`: One of classification labels (0-76) corresponding to unique intents.

	Intent names are mapped to `label` in the following way:

	\| label \| intent (category) \|
	\|---:\|:-------------------------------------------------\|
	\| 0 \| activate_my_card \|
	\| 1 \| age_limit \|
	\| 2 \| apple_pay_or_google_pay \|
	\| 3 \| atm_support \|
	\| 4 \| automatic_top_up \|
	\| 5 \| balance_not_updated_after_bank_transfer \|
	\| 6 \| balance_not_updated_after_cheque_or_cash_deposit \|
	\| 7 \| beneficiary_not_allowed \|
	\| 8 \| cancel_transfer \|
	\| 9 \| card_about_to_expire \|
	\| 10 \| card_acceptance \|
	\| 11 \| card_arrival \|
	\| 12 \| card_delivery_estimate \|
	\| 13 \| card_linking \|
	\| 14 \| card_not_working \|
	\| 15 \| card_payment_fee_charged \|
	\| 16 \| card_payment_not_recognised \|
	\| 17 \| card_payment_wrong_exchange_rate \|
	\| 18 \| card_swallowed \|
	\| 19 \| cash_withdrawal_charge \|
	\| 20 \| cash_withdrawal_not_recognised \|
	\| 21 \| change_pin \|
	\| 22 \| compromised_card \|
	\| 23 \| contactless_not_working \|
	\| 24 \| country_support \|
	\| 25 \| declined_card_payment \|
	\| 26 \| declined_cash_withdrawal \|
	\| 27 \| declined_transfer \|
	\| 28 \| direct_debit_payment_not_recognised \|
	\| 29 \| disposable_card_limits \|
	\| 30 \| edit_personal_details \|
	\| 31 \| exchange_charge \|
	\| 32 \| exchange_rate \|
	\| 33 \| exchange_via_app \|
	\| 34 \| extra_charge_on_statement \|
	\| 35 \| failed_transfer \|
	\| 36 \| fiat_currency_support \|
	\| 37 \| get_disposable_virtual_card \|
	\| 38 \| get_physical_card \|
	\| 39 \| getting_spare_card \|
	\| 40 \| getting_virtual_card \|
	\| 41 \| lost_or_stolen_card \|
	\| 42 \| lost_or_stolen_phone \|
	\| 43 \| order_physical_card \|
	\| 44 \| passcode_forgotten \|
	\| 45 \| pending_card_payment \|
	\| 46 \| pending_cash_withdrawal \|
	\| 47 \| pending_top_up \|
	\| 48 \| pending_transfer \|
	\| 49 \| pin_blocked \|
	\| 50 \| receiving_money \|
	\| 51 \| Refund_not_showing_up \|
	\| 52 \| request_refund \|
	\| 53 \| reverted_card_payment? \|
	\| 54 \| supported_cards_and_currencies \|
	\| 55 \| terminate_account \|
	\| 56 \| top_up_by_bank_transfer_charge \|
	\| 57 \| top_up_by_card_charge \|
	\| 58 \| top_up_by_cash_or_cheque \|
	\| 59 \| top_up_failed \|
	\| 60 \| top_up_limits \|
	\| 61 \| top_up_reverted \|
	\| 62 \| topping_up_by_card \|
	\| 63 \| transaction_charged_twice \|
	\| 64 \| transfer_fee_charged \|
	\| 65 \| transfer_into_account \|
	\| 66 \| transfer_not_received_by_recipient \|
	\| 67 \| transfer_timing \|
	\| 68 \| unable_to_verify_identity \|
	\| 69 \| verify_my_identity \|
	\| 70 \| verify_source_of_funds \|
	\| 71 \| verify_top_up \|
	\| 72 \| virtual_card_not_working \|
	\| 73 \| visa_or_mastercard \|
	\| 74 \| why_verify_identity \|
	\| 75 \| wrong_amount_of_cash_received \|
	\| 76 \| wrong_exchange_rate_for_cash_withdrawal \|

	### Data Splits

	\| Dataset statistics \| Train \| Test \|
	\| --- \| --- \| --- \|
	\| Number of examples \| 10 003 \| 3 080 \|
	\| Average character length \| 59.5 \| 54.2 \|
	\| Number of intents \| 77 \| 77 \|
	\| Number of domains \| 1 \| 1 \|

	## Dataset Creation

	### Curation Rationale

	Previous intent detection datasets such as Web Apps, Ask Ubuntu, the Chatbot Corpus or SNIPS are limited to small number of classes (<10), which oversimplifies the intent detection task and does not emulate the true environment of commercial systems. Although there exist large scale multi-domain datasets ([HWU64](https://github.com/xliuhw/NLU-Evaluation-Data) and [CLINC150](https://github.com/clinc/oos-eval)), the examples per each domain may not sufficiently capture the full complexity of each domain as encountered "in the wild". This dataset tries to fill the gap and provides a very fine-grained set of intents in a single-domain i.e. banking. Its focus on fine-grained single-domain intent detection makes it complementary to the other two multi-domain datasets.

	### Source Data

	#### Initial Data Collection and Normalization

	[More Information Needed]

	#### Who are the source language producers?

	[More Information Needed]

	### Annotations

	#### Annotation process

	The dataset does not contain any additional annotations.

	#### Who are the annotators?

	[N/A]

	### Personal and Sensitive Information

	[N/A]

	## Considerations for Using the Data

	### Social Impact of Dataset

	The purpose of this dataset it to help develop better intent detection systems.

	Any comprehensive intent detection evaluation should involve both coarser-grained multi-domain datasets and a fine-grained single-domain dataset such as BANKING77.

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[PolyAI](https://github.com/PolyAI-LDN)

	### Licensing Information

	Creative Commons Attribution 4.0 International

	### Citation Information

	```
	@inproceedings{Casanueva2020,
	author = {I{\~{n}}igo Casanueva and Tadas Temcinas and Daniela Gerz and Matthew Henderson and Ivan Vulic},
	title = {Efficient Intent Detection with Dual Sentence Encoders},
	year = {2020},
	month = {mar},
	note = {Data available at https://github.com/PolyAI-LDN/task-specific-datasets},
	url = {https://arxiv.org/abs/2003.04807},
	booktitle = {Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020}
	}
	```

	### Contributions

	Thanks to [@dkajtoch](https://github.com/dkajtoch) for adding this dataset.